| Computational (Neuro)phenomenology Model |
2024 |
Uses a coupled classifier and generative deep neural network to simulate visual hallucinations. |
• It can generate "synthetic VHs" characteristic of different aetiologies, including psychedelics. |
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| • It considers key dimensions like realism, dependence on sensory input, and complexity. |
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| • It uses Deep Convolutional Neural Networks (DCNNs) and Deep Generator Networks (DGNs), which could be adapted for our LSD-specific model. |
https://doi.org/10.3389/fnhum.2023.1159821 |
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| Serotonin 2A (5-HT2A) Receptor Activation Model |
2016 |
Directly relevant to LSD-induced hallucinations |
• It focuses on 5-HT2A receptor activation, crucial for LSD's effects. |
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| • It suggests that 5-HT2A receptor activation increases neuronal excitability and alters visual-evoked cortical responses. |
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| • This mechanism could be incorporated into our computational model to simulate LSD's effects on cortical activity. |
10.3390/ijms17111953 |
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| Visual Cortex AlterationsandThalamocortical Interactions |
2017 |
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• LSD increases functional connectivity between the primary visual cortex (V1) and other brain regions. |
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| • It alters spontaneous activity in retinotopically organized areas of V1 and V3, even with eyes closed. |
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| • These changes correlate with ratings of elementary or complex hallucinations. |
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| • LSD-induced changes in thalamocortical connectivity are linked to visual and auditory alterations. |
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| • This could be incorporated into our model to account for the role of thalamic gating in hallucinations. |
10.1038/npp.2017.86 |
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| Rebeus Model |
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